Maintaining a Sparse Inverse in the Simplex Method

Improved methods are discussed for handling sparse matrices in practical linear programming. An analytical comparison is made of four methods for updating the inverse in the iterations following a reinversion. Of these, one technique using the elimination form of inverse is selected for some computational experiments and its advantages in terms of speed and sparseness demonstrated.